The Map Equation framework.
A coding-theoretic approach to community detection: good communities are the ones that compress the description of a random walk best.
Since 2008, the framework has grown into Infomap (the reference algorithm), a family of visualizations, and ongoing research on higher-order, multilayer, and Bayesian community detection.
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May 5, 2026
Infomap v2.10
Per-level module counts in JSON output, idiomatic R package with SWIG bindings, library-safe no-output mode (changelog).
May 5, 2026
Infomap is now on R-universe
After many requests, Infomap is now available as an idiomatic R package with
SWIG bindings. Install it from R-universe
and run multilevel community detection directly from R — including a
high-level cluster_infomap() helper that takes an edge list and returns
modules, codelength, and a tidy node table. This brings the same algorithm
and feature set as the Python API to the R community.
Feb 25, 2026
Infomap v2.9
Enhanced CLI summary output, codelength correction for higher-order solutions, NetworkX multilayer graph state ID handling (changelog).
Feb 3, 2026
Community Detection with the Map Equation and Infomap: Theory and Applications
Jan 23, 2026
Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks
“The best maps convey a great deal of information but require minimal bandwidth: the best maps are also good compressions.”
M. Rosvall and C. T. Bergstrom, PNAS 105, 1118 (2008)